arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.00745eess.SPcs.LGcs.SYeess.SY
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基于机器学习技术的MmWave城市通信无线信道模型路径损耗预测

Predicting the Path Loss of Wireless Channel Models Using Machine Learning Techniques in MmWave Urban Communications

University of South Florida(南佛罗里达大学)

Saud Aldossari, Kwang-Cheng Chen

摘要:

经典的无线通信信道建模使用确定性和随机信道方法。机器学习(ML)的出现彻底改变了5G及以后的系统设计。机器学习技术如监督学习方法将用于预测无线信道路径损耗的变化的环境基于一定的数据集。通信系统基础的传播信号集中于信道建模,特别是针对诸如MmWave的新频段。由于部分相关的信道测量数据和模型的可用性,机器学习可以促进用于5G和超越无线通信系统的快速信道建模。当无线信道的不规则性导致实现准确模型的复杂方法时,适当的机器学习方法可以降低复杂性并提高准确性。在本文中,我们展示了传统信道建模之外的替代程序,以使用机器学习技术增强路径损耗模型,以减轻信道复杂性和测量耗时过程的困境。该演示回归使用特定场景的测量数据来成功地辅助不同操作环境的路径损耗模型的预测。

英文摘要:

The classic wireless communication channel modeling is performed using Deterministic and Stochastic channel methodologies. Machine learning (ML) emerges to revolutionize system design for 5G and beyond. ML techniques such as supervise leaning methods will be used to predict the wireless channel path loss of a variate of environments base on a certain dataset. The propagation signal of communication systems fundamentals is focusing on channel modeling particularly for new frequency bands such as MmWave. Machine learning can facilitate rapid channel modeling for 5G and beyond wireless communication systems due to the availability of partially relevant channel measurement data and model. When irregularity of the wireless channels lead to a complex methodology to achieve accurate models, appropriate machine learning methodology explores to reduce the complexity and increase the accuracy. In this paper, we demonstrate alternative procedures beyond traditional channel modeling to enhance the path loss models using machine learning techniques, to alleviate the dilemma of channel complexity and time-consuming process that the measurements were taken. This demonstrated regression uses the measurement data of a certain scenario to successfully assist the prediction of path loss model of a different operating environment.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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